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Zeng, Zhi,Hincapie, Marina,Pitteri, Sharon J.,Hanash, Samir,Schalkwijk, Joost,Hogan, Jason M.,Wang, Hong,Hancock, William S. American Chemical Society 2011 ANALYTICAL CHEMISTRY - Vol.83 No.12
<P>The discovery of breast cancer associated plasma/serum biomarkers is important for early diagnosis, disease mechanism elucidation, and determination of treatment strategy for the disease. In this study of serum samples, a multidimensional fractionation platform combined with mass spectrometric analysis were used to achieve the identification of medium to lower abundance proteins, as well as to simultaneously detect glycan and abundance changes. Immuno-affinity depletion and multi-lectin chromatography (M-LAC) were integrated into an automated HPLC platform to remove high abundance protein and fractionate glycoproteins. The collected glycoproteomes were then subjected to isoelectric focusing (IEF) separation by a digital ProteomeChip (dPC), followed by in-gel digestion and LC–MS analysis using an Orbitrap mass spectrometer. As a result, the total number of identified proteins increased significantly when the IEF fractionation step was included as part of the platform. Relevant proteins with biological and disease significance were observed and the dynamic range of the serum proteome measurement was extended. In addition, potential glycan changes were indicated by comparing proteins in control and cancer samples in terms of their affinity to the multi-lectin column (M-LAC) and the p<I>I</I> profiles in IEF separation. In conclusion, a proteomics platform including high abundance protein depletion, lectin affinity fractionation, IEF separation, and LC–MS analysis has been applied to discover breast cancer-associated proteins. The following candidates, thrombospondin-1 and 5, alpha-1B-glycoprotein, serum amyloid P-component, and tenascin-X, were selected as promising examples of the use of this platform. They show potential abundance and glycan changes and will be further investigated in future studies.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancham/2011/ancham.2011.83.issue-12/ac2002802/production/images/medium/ac-2011-002802_0006.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ac2002802'>ACS Electronic Supporting Info</A></P>
An Object-Level Feature Representation Model for the Multi-target Retrieval of Remote Sensing Images
Zeng, Zhi,Du, Zhenhong,Liu, Renyi Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.2
To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.
Zengzhi, Li,Qinrong, Fu,Feng, Lu Wen,Bin, Song Society for Computational Design and Engineering 2003 International Journal of CAD/CAM Vol.3 No.1
This paper presents a methodology in which the knowledge of design intents and change requests is communicated unambiguously cross collaboration partners through features. The domain of application is focused on the plastic part design for enabling effective collaboration between the product design and plastic mold making. The methodology takes the feature-based design approach and allows design features and knowledge to be reused in plastic injection mold design. It shortens the mold design lead-time, reduces mold design efforts, and enables unambiguous and fast design change management between product and mold designers. These contribute to the reduction of product development cycle time.